It is often desirable to sense the displacement of an item that has moved, or is repeatedly moving, relative to a frame of reference. By virtue of their relationship through a time variable, displacement sensing can often be achieved through velocity sensing, and vice versa. If the item in question is a known object, or at least a known blob, displacement sensing or velocity sensing can be achieved using a variety of methods, ranging from mathematically simpler interferometry or Doppler shift methods to highly complex image segmentation algorithms.
A more subtle scenario arises where the moving item offers little in the way of recognizable features, such as where a large sheet of markerless paper is moving past a magnifying glass, or where a featureless semiconductor substrate is moving past a microscope objective. So-called image flow methods, also referred to optical flow methods, have been used to determine displacements and/or velocities in such scenarios by processing sequential optical images of the surface of the moving item, such as those acquired by a CCD camera. Provided that the optically acquired images can reveal a sufficient amount of surface texture, image flow methods can be effective in computing the needed displacements and velocities. Indeed, some algorithms are capable of computing displacement to a level of precision greater than the pixel resolution of the CCD camera itself.
One issue arises when the moving item cannot or should not be optically imaged for the purposes of determining item displacement. By way of example, the required surface textures may be too small to be detected by optical imaging, or the application of visible light may damage the item's surface. There may be a variety of other reasons that optically imaging the item may be inapplicable, undesirable, inefficient, or impossible to achieve for purposes of displacement sensing.